MHCquant2 refines immunopeptidomics tumor antigen discovery

The identification of human leukocyte antigen (HLA)-presented peptides as targets of anti-cancer T cell response is pivotal for the development of novel immunotherapies. Mass spectrometry (MS)-based immunopeptidomics enables the detection of these peptides, yet confident identifications and thus implementation in immunotherapy design are hampered by the high diversity and low abundance of naturally presented HLA peptides. Here, we introduce MHCquant2, a Nextflow-based open-source pipeline that leverages OpenMS tools and peptide property predictors (DeepLC, MS ² PIP) for highly sensitive and scalable HLA peptide identification and quantification across various MS platforms. MHCquant2 increased peptide identifications up to 27% with a significant expansion of low-abundant peptides, outperforming state-of-the-art pipelines. Using MHCquant2 we build a comprehensive benign tissue repository comprising re-analyzed data from available benign immunopeptidomes and a novel benign _MHCquant2 dataset, adding more than 160,000 novel naturally presented HLA peptides. First applications of this benign repository and the MHCquant2 pipeline enabled (i) the refinement of tumor-associated antigens, (ii) the detection of novel, high-frequent tumor-exclusive peptide antigens for multiple tumor entities, and (iii) the identification and quantification of mutation-derived low-abundant neoepitopes. MHCquant2 refines tumor antigen discovery in immunopeptidomics, paving the way for the implementation of off-the-shelf and personalized immunotherapy design.